System and method for detecting ventilatory instability

ABSTRACT

Embodiments described herein may include systems and methods for detecting events that may be associated with sleep apnea. Some embodiments are directed to a system and/or method for automated detection of reduction in airflow events using polysomnograph signals, wherein the reduction in airflow events may relate to sleep apnea. The PSG signals may be limited to four signals, including data from an airflow channel, a blood oxygen saturation channel, a chest movement channel, and an abdomen movement channel. Using information from these channels, some embodiments may automatically identify reduction in airflow events.

BACKGROUND

The present disclosure relates generally to medical devices and methodsand, more particularly, to an automated system and method for detectingevents related to ventilatory instability, such as sleep apnea.

This section is intended to introduce the reader to various aspects ofart that may be related to various aspects of the present disclosure,which are described and/or claimed below. This discussion is believed tobe helpful in providing the reader with background information tofacilitate a better understanding of the various aspects of the presentdisclosure. Accordingly, it should be understood that these statementsare to be read in this light, and not as admissions of prior art.

Sleep apnea is generally described as a sleep disorder that ischaracterized by episodes of paused breathing during sleep. Theseepisodes of paused breathing may occur repeatedly throughout sleep, andeach episode may last long enough to cause one or more breaths to bemissed. Such episodes may be referred to as apneas. A typical definitionof an apnea may include an interval between breaths of at least 10seconds, with a neurological arousal and/or a blood oxygen desaturationof 3% or greater. The actual duration and severity of each apnea maysubstantially vary between multiple patients. Further, duration andseverity of apneas may vary throughout a period of sleep for a singlepatient. Indeed, sleep apnea may have a wide range of severity. Forexample, sleep apnea may include mild snoring, which may be related toincomplete and inconsequential airway obstruction, or severe apneas,which may result in hypoxemia. Sleep apnea commonly results in excessivedaytime sleepiness. Further, sleep apnea can hinder cognitive functionduring the day due to sporadic sleep during the night resulting fromrecurrent arousals associated with the sleep apnea.

Although sleep apnea commonly affects obese patients, it may occur inpatients with any body type. Indeed, sleep apnea is fairly common andcauses undesirable symptoms of excessive daytime sleepiness, morningheadache, and decreasing ability to concentrate during the day. Thus, itis desirable to diagnose and treat sleep apnea. Traditionally, sleepapnea is diagnosed utilizing an overnight sleep test referred to as apolysomnogram. This is generally performed in a sleep lab and involvesthe continuous and simultaneous measurement and recording of anencephalogram, electromyogram, extraoculogram, chest wall plethysmogram,electrocardiogram, measurements of nasal and oral airflow, and pulseoximetry. All or some of these and other channels may be measuredsimultaneously throughout the night, and complex recordings of suchmeasurement may then analyzed by a highly trained clinician to determinethe presence or absence of sleep apnea.

BRIEF DESCRIPTION OF THE DRAWINGS

Advantages of the disclosure may become apparent upon reading thefollowing detailed description and upon reference to the drawings inwhich:

FIG. 1 is a block diagram of a medical analysis system in accordancewith some embodiments;

FIG. 2 is a process flow diagram of a method for detecting ventilatoryinstability related events in accordance with some embodiments;

FIG. 3 is a block diagram of a device and/or module capable of receivingand filtering signals in accordance with some embodiments;

FIG. 4 is a process flow diagram of a method for estimating a value forbreathes per minute of a patient in accordance with some embodiments;and

FIG. 5 is a block diagram of the system of FIG. 1 communicativelycoupled with a separate ventilatory instability detection system tofacilitate calibration of the separate system in accordance with someembodiments.

DETAILED DESCRIPTION

One or more embodiments of the present disclosure will be describedbelow. In an effort to provide a concise description of the embodiments,not all features of an actual implementation are described in thespecification. It should be appreciated that in the development of anysuch actual implementation, as in any engineering or design project,numerous implementation-specific decisions may be made to achieve thedevelopers' specific goals, such as compliance with system-related andbusiness-related constraints, which may vary from one implementation toanother. Moreover, it should be appreciated that such a developmenteffort might be complex and time consuming, but would nevertheless be aroutine undertaking of design, fabrication, and manufacture for those ofordinary skill having the benefit of this disclosure.

Some embodiments are directed to automated systems and methods fordetecting events that may be associated with sleep apnea. Specifically,some embodiments are directed to a system and/or method for automateddetection of reduction in airflow events using polysomnograph (PSG)signals, wherein the reduction in airflow events may relate to sleepapnea. The PSG signals may be limited to four signals, including datafrom an airflow channel, a blood oxygen saturation (SpO₂) channel, achest movement channel, and an abdomen movement channel. Usinginformation from these channels, some embodiments may automaticallyidentify reduction in airflow events.

Accordingly, some embodiments may facilitate automated detection and/ordiagnosis of sleep apnea in patients. For example, some embodiments maybe utilized to analyze data that has been acquired using a separate PSGsystem to determine whether sleep apnea related events have occurred. Inanother example, some embodiments may be incorporated with a PSG systemto automatically detect and/or diagnose sleep apnea while a patient isbeing monitored. Indeed, some embodiments may facilitate detection ofevents relating to sleep apnea and/or facilitate diagnosis of sleepapnea in real time. Further, some embodiments may be utilized todemonstrate or confirm the accuracy or reliability of other systemsand/or methods for detecting events related to sleep apnea. For example,some embodiments may be utilized in conjunction with a device configuredto identify ventilatory instability (e.g., sleep apnea) based on an SpO₂pattern recognition algorithm.

In some embodiments, a distinction may be made between whetheridentified sleep apnea events correspond to central sleep apnea orobstructive sleep apnea. Central and obstructive sleep apnea may bedistinguished based on the nature of their occurrence. For example, alack of effort in breathing is generally the cause of interruptedbreathing associated with central sleep apnea, while a physical block inairflow despite effort is generally the cause of interrupted breathingassociated with obstructive sleep apnea. If a patient is or is notmaking an effort to breathe, the patient's chest and abdomen activitymay be indicative. Accordingly, some embodiments may distinguish betweenthe two types of apnea by including devices or modules that are capableof quantifying phase differences between chest and abdomen signals. Forexample, some embodiments may include an algorithm stored on a memorythat receives chest and abdomen signals and determines that certainevents correspond to obstructive sleep apnea when the chest and abdomensignals are out of phase, or that the events correspond to central sleepapnea when there is no chest and/or abdomen movement, or there is adecrease in chest and abdomen movement but signals are in phase.

FIG. 1 is a block diagram of a medical analysis system in accordancewith some embodiments. The medical analysis system is generallyindicated by reference numeral 10. The system 10 includes a PSG system12 and an event detection (ED) system 14. The PSG system 12 and the EDsystem 14 may be separate or integrated in accordance with someembodiments. The PSG system 12 may include, for example, hardware and/orsoftware products from Nellcor Puritan Bennett's Sandman® sleepdiagnostics line of products.

According to an embodiment, the PSG system 12 may include a memory 16and a processor 18, and the ED system 14 may include a memory 20 and aprocessor 22. Programming stored on the respective memories 16 and 20for the PSG system 12 and the ED system 14 may be utilized inconjunction with each system's respective processor 18 and 22 tofacilitate performance of certain functions by the PSG system 12 and theED system 14. For example, the memories 16 and 20 may include codedinstructions that may be utilized with the respective processors 18 and22 to perform automated methods in accordance with some embodiments. Itshould be noted that, in some embodiments, the memories 16 and 20 may beintegral with the respective processors 18 and 22. Further, if the PSGsystem 12 and ED system 14 are integral components of the system 10, theintegrated PSG system 12 and ED system 14 may share a single memoryand/or a single processor.

According to an embodiment, the PSG system 12 may be capable ofreceiving signals from various sensors 24 that are capable of measuringcertain physiologic parameters or patient activities. Specifically, thesensors 24 may include an airflow sensor 26, a chest sensor 28, anabdomen sensor 30, and a pulse oximeter sensor 32. Each of the sensors24 may be attached to a patient 34 to facilitate measuring physiologicparameters and/or physical activity (e.g., movement) of the patient 34.These four measured values from the sensors 24 may be transmitted assignals to the PSG system 12 for processing. Thus, the sensors 24 maycooperate with the PSG system 12 to provide a polysomnogram. Apolysomnogram may be described as a recording of an individual's sleepcharacteristics (e.g., activities and physiological events occurringduring sleep), which may include an output of various measurementsobtained by the sensors 24. Such an output may be recorded oil a memory(e.g., a flash memory or hard drive), presented on other tangible medium(e.g., printed on paper), or visually displayed (e.g., displayed asvideo). For example, the polysomnogram, along with other data, may bedisplayed on a video screen 36 of the medical analysis system 10.

According to an embodiment, the ED system 14 may be capable of usingdata acquired by the PSG system 12, such as the polysomnogram, toautomatically detect events that may be associated with sleep apnea. Forexample, the ED system 14 may automatically detect reduction in airflowevents, which relate to sleep apnea, using four PSG signals from the PSGsystem 12. The PSG signals may be transferred to the ED system 14 fromthe PSG system 14 in a number of ways. For example, data correspondingto signals from the PSG system 12 may be manually entered into the EDsystem 14, transferred via a memory device (e.g., a flash memory), ordirectly transmitted to the ED system 14 from the PSG system 12 foranalysis. The PSG system 12 and the ED system 14 may receive and/ortransmit signals corresponding to particular signal types based on datain a memory (e.g., a flash memory or a memory of the PSG system 12)and/or directly from each of the sensors 24. The PSG signals and/orsignal data may be referred to as channels corresponding to each signal,such as airflow, chest, abdomen, and pulse oximetry channels.

According to an embodiment, once the ED system 14 receives the PSGsignals, which may be in the form of signal data, the ED system 14 mayperform an automated process on the PSG signals to identify eventsrelated to sleep apnea, such as reduction in airflow events. If certainevents are identified in accordance with specified rules or criteria,the ED system 14 may provide an indication of the presence ofventilatory instability. For example, the ED system 14 may includehardware and/or software components that filter one or more of the PSGsignals and identify characteristics of the signals that combine tosuggest the presence of events related to sleep apnea. Specifically, forexample, after checking for invalid data, filtered and unfiltered PSGsignals may be utilized by the ED system 14 to estimate a value ofbreathes per minute (BPM) of the patient 34. The estimated BPM may thenbe utilized by the ED system 14 to calculate a baseline for use indetermining whether a threshold level of airflow reduction is present.Further, the ED system 14 may determine whether certain other eventsindicative of respiratory instability are present based on the PSGsignals, as will be discussed in further detail below. The ED system 14may output values indicative of whether respiratory instability ispresent based on whether certain events were identified. For example,the ED system 14 may indicate the presence of sleep apnea and/orindicate a type of sleep apnea (e.g., central or obstructive) based onresults obtained through analysis of a neural network of the ED system14. It should be noted that the identification of events by the EDsystem 14 may be facilitated by an algorithm stored in the memory 20,which may cooperate with the processor 22 to implement methods inaccordance with some embodiments. It should further be noted that thealgorithm may be tuned by adjusting constant values in the algorithm tocorrespond to particular situations or patients (e.g., a patient with aheart condition).

FIG. 2 is a process flow diagram illustrating a method in accordancewith some embodiments. The method is generally indicated by referencenumber 100 and includes various steps or actions represented by blocks.It should be noted that the method 100 may be performed as an automatedprocedure by a system, such as the analysis system 10. Further, certainsteps or portions of the method may be performed by separate devices.For example, a first portion of the method 100 may be performed by thePSG system 12 and a second portion of the method 100 may be performed bythe ED system 14. In this embodiment, the method includes receiving andfiltering signals (block 102), checking for invalid data (block 104),estimating BPM (block 106), determining a signal baseline for certainsignals (block 108), determining whether certain criteria are met byacquired data (block 110), and outputting a result (block 112).

According to an embodiment, the method 100 begins with receiving andfiltering signals, which may include signal data, as represented byblock 102. Block 102 may include receiving and/or filtering signal datafrom several sensors, which may include the airflow sensor 26, the chestsensor 28, the abdomen sensor 30, and/or the pulse oximeter sensor 32.Specifically, block 102 may represent receiving data or signals fromairflow, chest, abdomen, and pulse oximetry channels and filteringsignals on a subset of the channels. Signals on the chest, airflow, andabdomen channels may be filtered, while signals on the pulse oximetrychannel, which may relate to SpO₂ data, may remain unfiltered. Forexample, FIG. 3 illustrates a component 300 capable of filtering thechest, airflow, and abdomen channels in accordance with someembodiments. In some embodiments, the component 300 may be a feature ofthe processor, or it may be implemented using electronic circuits whichpreprocess and filter some or all of the PSG signals. 22. Specifically,as illustrated in FIG. 3, a raw signal 302 from the PSG system 12, suchas a signal on the chest, airflow or abdomen channel, may be receivedinto the component 300 of the ED system 114. The raw signal 302 may passthrough the component 300, which may include passing the raw signalthrough a band-pass filter 304 and a low-pass filter 366 of thecomponent 300.

According to an embodiment, the band-pass filter 304 may passfrequencies within a selected range and attenuate frequencies that areoutside of the selected range. Specifically, for example, the band-passfilter 30 may include a 0.0833-1 Hz band-pass filter that operates topass frequencies corresponding to 5-60 BPM and filter out frequenciesabove and below that range. An initial or default 5-60 BPM pass band maybe used since most human breathing rates will be within this range undertypical sleep-lab conditions. The pass band may be tuned (moved,narrowed, and/or widened) by the operator based on known patientconditions to increase the specificity and/or sensitivity of the EDsystem. Once the raw signal 302 has passed through the band-pass filter304, it becomes a band-passed signal 308. The band-passed signal 308 maybe utilized in conjunction with other input in the analysis andidentification of ventilatory instability, as will be discussed infurther detail below.

According to an embodiment, the low-pass filter 306 may pass frequenciesbelow a cutoff level and attenuate frequencies higher than the cutofflevel. Specifically, for example, the low-pass filter 306 may attenuatefrequencies lower than 0.04166 Hz, which corresponds to 2.5 BPM. 2.5 BPMmay be selected as the initial cutoff level since it is unlikely thatany patient will breathe slower than this rate, and thereforefrequencies below this level can be considered noise, or morespecifically these frequencies can be considered the DC offset of thePSG signal. The operator may move the low-pass filter level based onknown patient conditions in an attempt to increase the specificity andsensitivity of the ED system. Once the raw signal 302 has passed throughthe low-pass filter 306, it becomes a low-passed signal 310. Thelow-passed signal 310 may be used to calculate a noise signal 312 bypassing it through a subtraction block 314 with the raw signal 302 toget a difference between the raw signal 302 and the low-passed signal310. This and other features of noise calculation will be discussed infurther detail below. The calculated noise level may be utilized toclarify signal features by reducing noise content. In some embodiments,features may be included that facilitate reading data through the noise,rather than merely removing the noise.

According to an embodiment, after the signals have been received and/orfiltered in block 102, the method 100 may proceed to block 104, whichincludes checking for invalid data. Specifically, block 104 mayrepresent receiving and processing a stream of data to determine whetherany portions of the data meet criteria indicating that the data shouldbe discarded. For example, block 104 may represent receiving a 10 minutesegment of data that is analyzed to determine if the following criteriaare present: (1) the SpO₂ signal is less than a designated value (e.g.,less than a value of 20% SpO₂, which may indicate that the sensor is offor disconnected), (2) the signal to noise ratio for a signal on theairflow channel is less than a designated value (e.g., 5 dB), (3) thesignal to noise ratio for a signal on the chest channel is less than adesignated value (e.g., 0 dB), and/or (4) a signal to noise ratio (SNR)for a signal on the abdomen channel is less than a designated value(e.g., 0 dB). If it is determined in block 104 that any of thedesignated criteria, such as the criteria set forth above, are presentfor a designated period, e.g., 2 minutes, within the 10 minute segmentof data, all or part of the 10 minute segment of data may be discardedas being invalid. In some embodiments, features may be included thatfacilitate reading through data with a SNR below a threshold, ratherthan merely invalidating data because of low SNR values.

It should be noted that the signal to noise ratios referenced above maybe automatically calculated in block 104 along with other determinationsrelating to identifying invalid data. For example, block 104 mayrepresent calculating the signal to noise ratio for a particular signalby first removing DC from the raw signal 302. That is, the low-passedsignal 310 (LPFilteredSignal) may be subtracted from the raw signal 302(RawSignal) to obtain a raw signal without DC (RawSignalNoDC). Thisprocedure may be represented by the following equation:RawSignalNoDC=RawSignal−LPFilteredSignal.

Noise may then be calculated by subtracting the band-passed signal 308(BPSignal) from the raw signal without DC. This procedure may berepresented by the following equation:Noise=RawSignalNoDC−BPSignal.

A statistical measure of magnitude, such as a root mean square, may thenbe obtained for both the noise and the band-passed signal, and thesignal to noise ratio (SNR) may be calculated by dividing the root meansquare of the band-passed signal (RMS(Signal)) by the root mean squareof the noise (RMS(Noise)), as represented by the following equation:SNR=RMS(Signal)/RMS(Noise).As will be appreciated, in some embodiments, different calculations orprocedures may be utilized to obtain the signal to noise ratio.

According to an embodiment, after invalid data has been removed in block104, the method 100 may proceed to determine an estimated BPM based onthe received data, as represented by block 106. As illustrated in FIG.4, the BPM estimate may be calculated using the chest, abdomen, andairflow signals. FIG. 4 is a process flow diagram of a method ofestimating BPM in accordance with some embodiments. The method ofestimating BPM is generally indicated by reference number 400 andincludes various steps or actions represented by blocks. As with theother steps of the method 100, the method 400 may be performed as anautomated procedure by the system 100 in accordance with someembodiments.

According to an embodiment, the method 400 begins by receivingband-passed airflow, chest, and abdomen signals, and converting thesignals to the frequency domain by performing a fast Fourier transform(FFT) on all three channels, as represented by block 402. Specifically,block 402 may represent determining an FFT for the three channels acertain number of times per time period. For example, block 402 mayrepresent calculating an FFT for the band-passed airflow, chest, andabdomen signals once every 2 minutes.

According to an embodiment, after performing the frequency conversion inblock 402, the method 400 may proceed to block 404, which representsnormalizing the frequency spectrums obtained in block 402. The procedurerepresented by block 404 may include various different types ofnormalization, which may result in ranging the frequency spectrums from0.0 to 1.0. For example, peak normalization may be performed by dividingthe amplitude at each point in the spectrum of each signal by themaximum amplitude of that particular spectrum. Thus, the normalizedspectrum may include intensities that range from as low as 0.0 to ashigh as 1.0. By normalizing the spectrums, certain discrepancies betweenthe spectrums may be removed to facilitate proper combination orcomparison of the different spectrums. It should be noted that eachspectrum may be based on a segment of data, such as a 10 minute segmentof recorded sensor data.

According to an embodiment, after the spectrums have been normalized inblock 404, all three frequency spectrums may be averaged, as representedby block 406, and an estimate of BPM may be calculated based on theaverage, as represented by block 408. Specifically, blocks 406 and 408may include averaging all of the histograms of the frequency spectrumsand selecting a frequency from the average that has the highest amountof energy to determine the estimate BPM for the associated segment ofdata. This procedure may be represented by the following equation:BPM Estimate=max(Average Frequency Spectrum).

According to an embodiment, the process of providing BPM estimates for asegment of data may be repeated for a series of data segments, asrepresented by arrow 410. The most recent BPM estimate obtained for adata segment may be referred to as the current estimate and thepenultimate BPM estimate obtained for a previous data segment may bereferred to as the previous estimate. In other words, once a new segmentof data has been received and processed, the BPM estimate that was thecurrent estimate may become the previous segment and the BPM estimatefor the most recent segment of data may become the current estimate. Asrepresented by block 412, the current and previous estimates may beaveraged to provide a final BPM estimate, as represented by thefollowing equation:Final BPM Estimate=0.5(Current Estimate+Previous Estimate).It should be noted that in some embodiments, more than two estimates maybe averaged to determine the final BPM estimate. For example, three ormore estimates for a series of data segments may be stored and averagedto determine a final BPM estimate in accordance with some embodiments.

According to an embodiment, after estimating BPM in block 106, themethod 100 may proceed to determining a signal baseline for each signal,as represented by block 108. Specifically, for example, block 108 mayinclude calculating a signal baseline for the airflow, chest, andabdomen signals. In performing this calculation, a root mean squarevalue for the signal (e.g., the raw signal 302) may first be obtainedusing the final BPM estimate and a running root mean square, asindicated by the following equation:Xrms=0.5 BPM running RMS signal X.This calculation may take into account every point of a signal such thatthe results are indicative of the power of the signal at each point. Thebaseline value (X-Baseline) for each particular signal may then bedetermined based on this root mean square value (Xrms). Specifically,the baseline may be determined as the top 10% or ninetieth percentile ofthe root mean square value, as represented by the following equation:X-Baseline=Top 10% (90th percentile) of Xrms.

According to an embodiment, once a baseline has been determined for theairflow, chest, and abdomen signals, as represented by block 108, themethod 100 may proceed with determining whether certain criteria aremet, as represented by block 110. The presence of characteristics thatmeet certain criteria may indicate and confirm that a reduction inairflow (RAF) event occurred relative to a normal airflow. For example,a determination may be made as to whether a certain level of reduction(e.g., a 40% reduction) in airflow has occurred for at least a thresholdamount of time (e.g., 10 seconds). This may be referred to as an RAFevent. Specifically, in one embodiment, an RAF event may be described asan interval where the amplitude envelope of a signal on the airflowchannel from the PSG system 12 is reduced at least 40% relative to thebaseline for at least 10 seconds consecutively. It should be noted thatthe amplitude envelope may refer to the value of a function describinghow the maximum amplitude of the airflow signal changes over time.

According to an embodiment, once an RAF event has been identified, itmay be qualified for consideration based on certain scoring rules. Forexample, an RAF event may be disqualified if there is not a specifiedamount of change in one or more other signal measurements within acertain window of time with respect to the time the RAF event occurred.For example, if the measured SpO₂ value does not change at least acertain amount, e.g., 3%, during the RAF event or within a certain time,e.g., 30 seconds, after the RAF event, the RAF event may bedisqualified. Similarly, for example, if there is not at least a certainchange, e.g., a 40% reduction, in the chest or abdomen signal from thePSG system 12 for at least part, e.g., half, of the interval of the RAFevent, the RAF event may be disqualified. Alternatively, if theavailable data meets these criteria, the RAF event may be qualified andused to determine whether the data is indicative of ventilatoryinstability in the patient that was or is being monitored.

According to an embodiment, block 110 may also represent determiningwhether a segment of data includes an indication of ventilatoryinstability based on a quantity of qualified RAF events that occurwithin the data segment. For example, block 110 may determine that aparticular data segment is indicative of ventilatory instability if acertain number, e.g., at least 5, of consecutive RAF events arequalified within a given portion, e.g., a 10 minute period, of thesegment of data. Thus, segments of data may be divided into intervals,e.g., 10 minute intervals, in accordance with some embodiments. Itshould be noted that in order for the RAF events to be qualified asbeing consecutive, certain relative timing criteria may have to be met.For example, in one embodiment, for a pair of qualified RAF events to beconsecutive, the RAF events must occur within a certain time, e.g., 120seconds, of one another.

According to an embodiment, block 110 may also represent determining atype of respiratory event, such as determining the presence of centralor obstructive sleep apnea based on correlations between differentsignals, such as the chest and abdomen signals being in or out of phase.As discussed above, central and obstructive sleep apnea may bedistinguished based on the nature of their occurrence. For example, alack of effort in breathing is generally associated with central sleepapnea, while a physical block in airflow despite effort is generallyassociated with obstructive sleep apnea. A patient's chest and abdomenactivity may be utilized to distinguish between the two types of apnea.Accordingly, some embodiments may include features that are capable ofquantifying phase differences between chest and abdomen signals. Forexample, some embodiments may include features that determine thatcertain events correspond to obstructive sleep apnea when the chest andabdomen signals are out of phase, or that the events correspond tocentral sleep apnea when there is no chest and/or abdomen movement, orthere is a decrease in chest and abdomen movement but signals are inphase.

Based on the criteria or rules discussed above, the method 100 mayoutput an epoch score for each data segment, as represented by block112. For example, an epoch score may be repeated once every time asegment of data has been analyzed (e.g., once every 10 minutes). In someembodiments, a system may utilize such a score in an algorithm toprovide an indication of certain conditions relating to ventilatoryinstability. For example, the epoch score may include a value of 1 foran indication that sleep apnea has been detected, 0 for an indicationthat sleep apnea has not been detected, or −1 for an indication ofinvalid data. The indicator provided by the system based on the scoremay include a textual indication of the detected condition, such as“sleep apnea detected,” “sleep apnea not detected”, or “unknown orinvalid data.” In some embodiments, a series of epoch scores may becombined for all or part of a sleep study to generate an aggregatescore. For example, an average of all of the scores for each 10 minutesegment of a sleep cycle may be used to determine a summary score for aparticular patient.

Further, in some embodiments, the severity of the ventilatoryinstability or apnea events may be quantified instead of merelyproviding binary outputs. For example, the depth of the airflowreduction may be used to quantify a severity of the ventilatoryinstability. In some embodiments different aspects associated withqualified RAF events may be used to determine levels of severity. Forexample, a series of continually larger drops in airflow and/orcontinual failures to return to a baseline level of airflow may becorrelated to a higher level of severity. Further, numerous clusters ofRAF events or data segments including RAF events may be considered indetermining a severity level. For example, a large segment of data,e.g., 4 hours of data, may include various sub-segments having differentlevels of apnea. Each sub-segment may be analyzed separately based oncertain criteria, such as a number of RAF events per time period, anamount of reduced airflow, or other characteristics, and the valuesassociated with the sub-segments may be combined to provide an overallventilatory instability level for the large segment of data.

The ED system 14 may be utilized to demonstrate or confirm that othersystems involving detection of ventilatory instability related eventsare properly calibrated and/or providing results that correlate to theresults obtained by the ED system 14. For example, FIG. 5 is a blockdiagram of the ED system 14 communicatively coupled with a separateventilation analysis system 500 to facilitate calibration of the system500 or to confirm its proper operation. The separate ventilationanalysis system 500 may include a system such as that described in U.S.Pat. No. 6,223,064. For example, the system 500 may include an SpO₂pattern recognition system that is utilized to identify ventilatoryinstability based on a series of SpO₂ values. The system 500 may includea memory 502 and a processor 504 that are capable of analyzing inputreceived or previously acquired from a single SpO₂ sensor. Specifically,the system 500 may be capable of identifying certain patterns orclusters of measured SpO₂ values to identify events related toventilatory instability.

According to an embodiment, the ED system 14 may facilitate adjustmentand/or calibration of the system 500 to correlate with BPM estimatesdetermined by the ED system 14. For example, both systems 14 and 500 maybe provided with data from a storage device 506, wherein the datacorresponds to certain ventilatory instability events that have beenobserved. During a calibration period, the system 500 may be adjusted tocorrespond to the ED system 14. In other words, the system 500 may beadjusted such that it detects a high percentage of the ventilatoryinstability events detected by the ED system 14. For example, certaincoefficients of an algorithm stored on the memory of the system 500 maybe adjusted to improve correlations between the results for the system500 and the results for the ED system 14.

In a specific example, the ED system 14 may utilize automated analysisof the data obtained via the various sensors 24 to confirm that thesystem 500 is properly tuned and/or providing corresponding output.Specifically, the ED system 14 may receive data from the storage device506 corresponding to airflow, chest impendence, abdomen impedance, andblood oxygen saturation, and use this data to provide results thatinclude a BPM estimate. These results may be compared with similarresults obtained by the system 500 using SpO₂ pattern recognition. Basedon the comparison, certain features of the system 500 (e.g., features ofa pattern recognition algorithm stored on the memory 502) may beadjusted to achieve a desired correspondence. The automated analysisprovided by the ED system 14 may facilitate rapid adjustment and/ortesting of systems such as the system 500.

Specific embodiments have been shown by way of example in the drawingsand have been described in detail herein. However, it should beunderstood that the claims are not intended to be limited to theparticular forms disclosed. Rather, the claims are to cover allmodifications, equivalents, and alternatives failing within their spiritand scope.

1. A system, comprising: a processing device configured to: calculate anoutput value of breathes per minute for a data segment based on signalsof an airflow channel, a chest channel, and/or an abdomen channel and/orcombinations thereof; calculate a baseline value for each of the airflowchannel, the chest channel, and the abdomen channel based at least inpart upon the output value of breathes per minute and a measure of themagnitude of the corresponding signal; determine whether a reduction inairflow above a minimum reduction level relative to the baseline valuefor the airflow channel has been maintained for a period of time andidentify a reduction in airflow event if the reduction in airflow isabove the minimum reduction level; determine if the reduction in airflowevent is qualified by determining whether a specified amount of changehas occurred in the chest channel or the abdomen channel during a windowof time including the reduction in airflow event; and provide anindication of ventilatory instability if the reduction in airflow eventis identified and qualified.
 2. The system of claim 1, comprising afilter capable of filtering the airflow channel, the chest channel,and/or the abdomen channel.
 3. The system of claim 2, comprising afilter capable of band-pass filtering and low-pass filtering the airflowchannel, the chest channel, and the abdomen channel, while passing thepulse oximetry channel.
 4. The system of claim 1, comprising a monitorcapable of displaying the indication of ventilatory instability.
 5. Thesystem of claim 1, wherein the processing device is configured todetermine whether the indication of ventilatory instability correspondsto central sleep apnea or obstructive sleep apnea based on signals ofthe chest channel and abdomen channel being in a phase or out of phase.6. The system of claim 1, comprising an integral polysomnograph systemcapable of providing the signals of the airflow channel, the chestchannel, and the abdomen channel.
 7. The system of claim 1, comprising apulse oximetry sensor; an airflow sensor, a chest sensor, and an abdomensensor.
 8. A system, comprising: a polysomnograph system capable ofsupplying signals of an airflow channel, a chest channel, an abdomenchannel, and a pulse oximetry channel; an event detection systemincluding a processing device configured to: calculate a baseline valuefor each of the airflow channel, the chest channel, and the abdomenchannel based at least in part upon a value of breathes per minute and ameasure of the magnitude of the corresponding signal; identify areduction in airflow event when a reduction in airflow above a minimumreduction level relative to the baseline value for the airflow channelhas been maintained for a threshold period of time; and determine if thereduction in airflow event is qualified by determining whether aspecified amount of change has occurred in the chest channel, theabdomen channel, or the pulse oximetry channel during a window of timeincluding the reduction in airflow event; and a pulse oximetry patternrecognition system capable of identifying ventilatory instability basedat least in part upon a series of blood oxygen saturation values; and asystem calibration component capable of comparing results from the eventdetection system and the pulse oximetry pattern recognition system tofacilitate adjustment of the pulse oximetry pattern recognition system.9. The system of claim 8, wherein the processing device of the eventdetection system is configured to calculate the value of breathes perminute for a data segment based on signals of the airflow channel, thechest channel, and/or the abdomen channel and/or combinations thereof.10. The system of claim 9, wherein the processing device of the eventdetection system is configured to calculate the value of breathes perminute by averaging an estimate of breathes per minute for each of aplurality of data segments based at least in part upon the signals ofthe airflow channel, the chest channel, and/or the abdomen channeland/or combinations thereof.
 11. The system of claim 9, comprising adisplay, wherein the event detection system is configured to provide anindication ofventilatory instability on the display if the reduction inairflow event is identified and qualified.
 12. The system of claim 8,wherein the processing device of the event detection system isconfigured to convert the signals of the airflow channel, the chestchannel, and/or the abdomen channel to a frequency domain, and whereinthe event detection system is configured to determine a plurality offrequency spectrums, wherein the plurality of frequency spectrumscomprises an airflow channel frequency spectrum, a chest channelfrequency spectrum, and/or an abdomen channel frequency spectrum. 13.The system of claim 12, wherein the processing device of the eventdetection system is configured to calculate the value of breathes perminute for a data segment based on determining a highest frequency ofrespective averages of the plurality of frequency spectrums.
 14. Thesystem of claim 8 wherein the system calibration component is capable ofadjusting features of the pulse oximetry pattern recognition system toachieve a desired correspondence between respective results of the pulseoximetry pattern recognition system and the event detection system.